In another reshuffle of its video game division, Netflix is closing the first studio it purchased, Oxenfree team Night School, according to Stephen Totilo at Game File. Netflix acquired Night School Studio in 2021, and there it produced Oxenfree II: Lost Signals, the Black Mirror: Thronglets app, and Unhinged, a smartphone-controlled horror game that landed roughly six weeks ago.

Netflix also reportedly plans to shut down Helsinki studio Moonloot Games, which the company founded in September 2022 to build original mobile titles. Additionally, it’s slashing an undisclosed number of jobs from its in-house games team. This is all in the name of prioritizing kids gaming, party games, narrative experiences and mainstream titles like June’s FIFA World Cup: Launch Edition, a Netflix spokesperson told Game File.

The FIFA World Cup game was developed by a third-party studio, Refactor Games, which laid off 85 percent of its staff in early August. The layoffs followed reports that Refactor’s parent company, Delphi Interactive, pulled funding after the game’s release.

In a real salt-rubbing move, Netflix co-CEO Gregory Peters praised Night School’s game Unhinged and FIFA World Cup during the company’s Q2 2026 earnings call on July 16.

“We’ve been building some solid foundations, and now we’re seeing exciting positive signals that help inform and give us increased conviction in our future growth and the nature of that growth here,” Peters said just a few weeks ago. “So you mentioned the cloud-based strategy, those cloud-based TV games, we really see it working. FIFA and Unhinged became our two most successful cloud game debuts, really solid numbers that put it in the top tier of game performance for us.”

Night School co-founders Sean Krankel and Adam Hines were supremely optimistic about the Netflix acquisition when I talked with them at Summer Game Fest 2023, just prior to the release of Oxenfree II. Netflix decided to establish a video game business in 2021, and according to Krankel, that’s when executives wooed the Night School founders with lines like, “What can we do to unblock your team from making your dreams?” 

Netflix officially acquired Night School in September 2021. After the purchase, Night School hired more team members and moved into the Netflix offices. In 2023, Krankel and Hines talked about how cool it was to be able to fly in remote workers as needed, and to make Oxenfree II available in 32 languages on day one. The founders discussed how at ease they felt at Netflix.

“Our big concern was the autonomy aspect,” Hines said at the time. “We’ve all worked at bigger studios before, and have just seen and felt how long it would take to get decisions made, how the creative would kind of get choked out of things because there’s too many cooks in the kitchen. But just talking to Netflix a lot before we joined up, we felt really at ease, just like we were talking the same language about how to make games.”

Netflix rapidly expanded its video game division between 2021 and 2024, purchasing a handful of teams including Cozy Grove creator Spry Fox and mobile developer Boss Fight Entertainment, and staffing an internal AAA studio with veterans from Overwatch, Halo and God of War franchises. Then in 2024, Netflix hired former Epic Games executive Alain Tascan to lead its games business, and everything changed.

With this week’s closure of Night School and Moonloot, Netflix has now shut down or sold all but one of its studios. Spry Fox, at least, was able to purchase itself back from Netflix and is still making ambitious, adorable games. The sole survivors at Netflix are Helsinki-based Next Games, which focuses on couch-based experiences, and Netflix Games Studio, which mostly signs deals with external developers.

That group seems to be going strong, with a slew of streamable party games like Overcooked! All You Can Eat and Jackbox titles hitting Netflix in recent months.

Google is rolling out Gemini 3.7 Flash, a new version of its workhorse AI model that puts coding, agentic workflows and knowledge work at the center of the upgrade — while temporarily cutting API prices in half.

The release arrives just three weeks after the release of Gemini 3.6 Flash, an unusually short turnaround that Google attributes to developer feedback and algorithmic improvements.

For enterprise developers, the more consequential story may be the combination of those intelligence gains with lower inference costs: through the end of 2026, Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens.

Starting Jan. 1, 2027, pricing rises to $1.50 per million input tokens and $7.50 per million output tokens. That means the current discount is temporary, but it gives teams deploying high-volume coding and business agents several months to evaluate whether Google’s claimed reductions in retries and manual oversight translate into lower total operating costs.

The launch also underscores Google’s rapid iteration on its Flash line while its next flagship Pro model remains absent. Google did not provide a release date for Gemini 3.5 Pro with Thursday’s announcement, Reuters reported, despite the model having previously been described as undergoing partner testing. Axios similarly noted that 3.7 Flash arrives before the anticipated Pro release.

A three-week upgrade focused on getting work done

Google describes Gemini 3.7 Flash as its “most intelligent workhorse model yet for coding and agents.” The company says the model is better at adapting when it encounters roadblocks, clarifying intent when necessary and following instructions with greater fidelity.

Those improvements matter beyond benchmark scores. In an enterprise coding agent, a model that makes fewer unnecessary changes, recovers from errors and executes multi-step plans more reliably can reduce the number of human interventions needed to complete a task. The same principle applies to business agents operating across documents and applications, where an incorrect tool call or poorly interpreted instruction can derail an otherwise useful workflow.

Google says 3.7 Flash “thinks more diligently,” applying more effort to multi-step planning and tool calls. Its stated goal is more disciplined execution with fewer retries and less manual supervision.

That represents an interesting evolution from Gemini 3.6 Flash. Google’s developer documentation described 3.6 as reducing reasoning steps, conversational turns and tool calls compared with earlier models while attempting to limit execution-loop spiraling. With 3.7, the emphasis shifts toward putting sufficient effort into planning while improving the quality of execution — potentially a more useful optimization than simply minimizing the number of steps an agent takes.

Google DeepMind said in a post accompanying the release that 3.7 Flash shows gains in debugging and issue resolution, generates more functional web layouts and applications with fewer prompts, and improves reasoning and accuracy on real-world business workflows.

Coding gains are substantial, but not universal

Google’s benchmarks show a large generational improvement in several software engineering tests.

On FrontierCode 1.1 Main, which measures production code quality, Gemini 3.7 Flash scores 43.6%, up from 34.4% for Gemini 3.6 Flash. That also narrowly exceeds the 42.7% Google reports for Claude Sonnet 5 and 41.3% for GPT-5.6 Terra.

On DeepSWE v1.1, a long-horizon software engineering evaluation, 3.7 Flash reaches 65.3%, compared with 49.0% for its predecessor. GPT-5.6 Terra remains ahead at 69.6% in Google’s table.

Web development shows another notable gain. Gemini 3.7 Flash receives an Elo score of 1588 on Code Arena, versus 1538 for 3.6 Flash, 1541 for Claude Sonnet 5 and 1523 for GPT-5.6 Terra. Google says the new model can produce more functional layouts and feature-complete applications in fewer prompts while more closely following reference screenshots, images and design systems.

The broader benchmark table is more mixed, which is important for enterprises evaluating the model against particular workloads rather than looking for a single “best” model.

Gemini 3.7 Flash scores 85.8% on Terminal-bench 2.1, compared with 87.4% for GPT-5.6 Terra. Terra also leads Google’s comparisons on Terminal-bench 3.0 and OSWorld-2.0. Claude Sonnet 5 leads the Agent’s Last Exam multimodal desktop and operating-system tasks with a 33.3% pass rate, versus 26.3% for Gemini 3.7 Flash.

In other words, Google’s own results do not show 3.7 Flash universally displacing higher-priced competitors. They instead suggest a model that has become substantially more competitive in coding and agent workloads while occupying a lower price tier.

Enterprise workflows may be the more important test

The gains extend beyond software development.

On AutomationBench, which Google describes as measuring enterprise workflow automation, Gemini 3.7 Flash scores 30.4%, up sharply from 17.0% for 3.6 Flash. Google’s table lists Claude Sonnet 5 at 10.7% and GPT-5.6 Terra at 23.6%.

The model also reaches 34.0% on GDP.PDF, an evaluation of complex PDF comprehension, compared with 22.0% for 3.6 Flash, 28.0% for Claude Sonnet 5 and 24.7% for GPT-5.6 Terra.

That combination is relevant for enterprise agents because many practical deployments require more than generating text or code. An agent may need to interpret a long report, identify relevant information, decide which tool to invoke, update another system and produce a document for a human reviewer. Reliability across that chain can matter more than performance on an isolated reasoning benchmark.

Google is putting that thesis into practice with Gemini Spark. Google AI Pro and Ultra subscribers can use 3.7 Flash in Spark, the company’s personal AI agent. Google says the upgrade improves Spark’s knowledge work and tool use across Google Workspace applications, including workflows that consolidate files, draft emails and update status documents.

For enterprises, 3.7 Flash is also available through the Gemini Enterprise Agent Platform and Gemini Enterprise app.

Price becomes part of the model competition

Gemini 3.7 Flash’s introductory pricing is a notable bid to embed the model into enterprise workflows.

Until Dec. 31, developers pay $0.75 per million input tokens and $3.75 per million output tokens. Context caching costs $0.075 per million tokens during the introductory period. Google says standard prices will double on Jan. 1, 2027, to $1.50 for input and $7.50 for output, with context caching rising to $0.15.

For comparison, Gemini 3.6 Flash’s standard API pricing is $1.50 per million input tokens and $7.50 per million output tokens. Google’s benchmark table lists Claude Sonnet 5 at $2 and $10, respectively, while GPT-5.6 Terra is listed at $2 and $12.

The economics become more pronounced for autonomous agents because a single user request can produce a long sequence of model calls, reasoning tokens and tool interactions. A model that costs less per token but requires substantially more retries may not ultimately be cheaper.

Conversely, Google’s combination of lower introductory token pricing and claimed improvements in first-pass accuracy could materially change the cost of running high-volume coding or document-processing agents if those gains carry over to production.

That is the metric enterprise teams will ultimately need to test: not price per million tokens in isolation, but cost per successfully completed task.

Google’s AI shake-up raises the stakes for Gemini

Gemini 3.7 Flash arrives amid a broader debate over whether Google is losing ground at the AI frontier. The company has not released Gemini 3.5 Pro, despite saying in May that the flagship model would arrive the following month.

By July, Google said it remained in partner testing and would become broadly available when ready; Thursday’s announcement offered no further timetable. Google’s latest released general-purpose Pro model therefore remains Gemini 3.1 Pro, introduced in February.

Reuters reported in July that Gemini 3.5 Pro missed its original target after falling short of internal goals, particularly in coding, even as Google began training what it calls its most ambitious model yet, Gemini 4.

The delay coincides with a major overhaul of Google’s AI leadership announced last week.

Google DeepMind co-founder and Nobel Prize Winner Demis Hassabis has relinquished day-to-day control of the company’s famed DeepMind AI division to become its chair and, simultaneously, to take on the role of Alphabet’s chief scientist.

Meanwhile, former DeepMind CTO Koray Kavukcuoglu now runs the unit as a senior vice president reporting directly to CEO Sundar Pichai.

Kavukcuoglu controls Gemini model development, frontier research, the Gemini app and developer teams—effectively consolidating the full Gemini chain under a more product-focused operator.

Chief scientist Jeff Dean, Gemini co-lead Oriol Vinyals, Quoc Le and Sanjay Ghemawat left to establish the research startup Discovery Loop.

Those exits followed Gemini co-lead Noam Shazeer’s move to OpenAI and Nobel Prize-winning AlphaFold scientist John Jumper’s departure for Anthropic. Reuters reported that internal disagreements, constrained compute allocation and Google’s bureaucracy contributed to slower releases and weaknesses in coding.

Outside interpretations range from organizational repair to a more fundamental retreat.

SemiAnalysis has argued that Google is increasingly prioritizing the highly profitable business of supplying cloud infrastructure to AI companies—including Gemini competitors—over keeping its own models at the absolute frontier. That analysis also claimed Google had effectively canceled 3.5 Pro, although Google has not confirmed that and continues to describe the model as delayed.

The Verge offered a more measured assessment: the departures and model delays are serious, but Google retains enormous advantages through Search, Workspace, Android, Cloud, custom AI chips and consumer distribution. Google says the Gemini app has surpassed 950 million monthly users, giving it a reach that does not depend entirely on owning the highest-scoring model.

Current benchmarks similarly depict a company behind the overall leaders but still firmly competitive. Artificial Analysis places Claude Opus 5 at 63 on its overall model Intelligence Index, while Google reports a score of 56 for Gemini 3.7 Flash—an improvement from 52 for 3.6 Flash but not a return to the top.

Arena’s early human-preference results are more favorable, provisionally ranking 3.7 Flash ninth overall and eighth for web development.

The resulting picture is not that Google has abandoned advanced AI, but that it has become stronger at rapidly shipping efficient Flash models while struggling to deliver the premium flagship required to reclaim broad leadership. Gemini 4 will now serve as the clearest test of whether the leadership reorganization fixes that execution gap.

Available now across Google’s developer stack

Developers can access Gemini 3.7 Flash through the Gemini API in Google AI Studio and Android Studio, as well as Google’s Antigravity environment. Enterprises can deploy it through Gemini Enterprise Agent Platform and Gemini Enterprise, while consumers with Google AI Pro or Ultra subscriptions can access the model through Spark in supported countries.

Google is also shipping updated safeguards covering chemical, biological, radiological and nuclear risks and cyber-offense misuse, according to the company.

The unusually fast jump from Gemini 3.6 Flash to 3.7 Flash points toward a model development cycle in which algorithmic improvements can reach production products without waiting for a new flagship generation. Ars Technica also highlighted the three-week interval between the two releases, while Google says the techniques behind the update will inform future models.

For developers, that faster cadence creates its own operational question. Models can improve quickly, but production teams still have to benchmark new releases against their own repositories, prompts, tool schemas and failure modes before changing a deployment.

Gemini 3.7 Flash gives those teams a particularly strong incentive to run that evaluation. Google’s own numbers show major improvements in production coding, web development, document comprehension and workflow automation without claiming leadership everywhere. At its introductory price, Google is effectively betting that developers will value a model that is competitive enough with more expensive systems while being cheap enough to run repeatedly inside agents.

Whether that advantage survives the return to full pricing in January will depend less on leaderboard positions than on how reliably 3.7 Flash completes real work.

Databricks has closed a $5bn round at a $190bn valuation, led by Coatue, with revenue run-rate past $7bn and growth above 80% year on year. That is a 42% valuation increase in six months, and it comes after chief executive Ali Ghodsi called 2026 a bad year to go public.

Databricks has closed $5bn at a $190bn valuation. Coatue led, joined by Blackstone, MGX, T. Rowe Price and new investor Sixth Street Growth. It is the company’s second round this year.

The growth is the part worth pausing on. Revenue run-rate has passed $7bn, up more than 80% year on year in the second quarter, against 65% growth at a $5.4bn run-rate back in February. Companies of this size do not usually accelerate.

The valuation has moved with it, up 42% from $134bn in February. TNW reported the round at $188bn last month, and it has closed $2bn above that.

Databricks also disclosed figures that rarely accompany a private raise. It says it has been adjusted free cash flow positive over the last twelve months, its data warehousing business is past a $1.5bn run-rate and growing over 100%, and its Lakebase database has passed $100mn.

The customer concentration is heavy at the top. More than 1,000 accounts now spend at a $1mn run-rate, and more than 100 at $10mn.

Set against the public market, the price is less extravagant than it sounds. At $190bn on a $7bn run-rate, Databricks is valued at roughly 27 times revenue, while Snowflake trades near 23 times on $5.03bn of trailing revenue.

The growth rates are nothing like each other. Snowflake grew around 30% last year, Databricks says more than 80%, so a four-point multiple premium is a modest reward for nearly three times the pace.

Read those numbers with care, because they are not the same measure. Run-rate annualises current revenue and flatters anything growing quickly, while Snowflake’s multiple rests on twelve trailing months.

Why this is private money rather than a listing is already on the record. Ghodsi has called 2026 a terrible year to go public, with SpaceX, OpenAI and Anthropic lined up to absorb roughly $200bn of listing capital.

So the company raises at public scale and stays private. The money goes to three products aimed at enterprise AI agents, and Ghodsi’s pitch is that buyers want agents that hold context, stay accurate and respect a budget, rather than another chatbot.

Evergreen Candy Crush is one of the most successful and enduring mobile game franchises ever created. Launched by Swedish video game developer and publisher King in 2012, the original Candy Crush Saga quickly climbed the popularity charts on both Android and iOS. Nearly a decade and a half after its launch, it still boasts millions of users and generates billions of dollars in revenue.

In FY 2025, Candy Crush Saga reportedly generated $876.5 million in revenue, with most of it coming from in-app purchases. While that sounds impressive, it marks a slight dip from the previous six years, when the game generated more than $1 billion in annual revenue.

Candy Crush Saga follows a freemium model, meaning it’s free to play, but players can purchase extra lives, boosters, and special passes to make it easier to progress through its more than 23,000 levels.

While King hasn’t disclosed detailed revenue and profit figures in recent years, it reported surpassing $20 billion in revenue in 2023. That same year, the company also claimed to have reached 5 billion installs across all Candy Crush titles, including the original Candy Crush Saga and its spinoffs Soda Saga, Jelly Saga, and Friends Saga.

Players at the Candy Crush Saga All Stars esports event in Los Angeles in 2025.

Research by David Curry, data editor at app analytics platform Business of Apps, suggests that the original game currently has around 81.5 million monthly active users and was installed more than 190 million times across Android and iOS in 2025.

In its 2014 IPO filing, King claimed that Candy Crush Saga had 93 million daily active users worldwide in December 2013. If Curry’s estimates are accurate, this would suggest that the game has not experienced a massive decline in its user base over the past 12 years.

Candy Crush’s enduring popularity remains something of a mystery among the mobile gaming community. However, some believe its success can partly be attributed to its “turn-based” gameplay, which allows players to put the game down at any time and pick it up where they left off without penalty.

Paula Ingvar, general manager of Candy Crush Saga, attributes the game’s success to giving players “the satisfaction of making progress with small wins” in 10- to 20-minute sessions, rather than forcing them to finish a level in one sitting.

David Nieborg, a professor of media and platform studies at the University of Toronto and a video game researcher, believes the game’s popularity also has a lot to do with the fact that it is “non-place-based, nongendered, nonracialized,” helping it appeal to players across geographical, linguistic, cultural, and socioeconomic boundaries.

Apple is expanding its U.S. manufacturing footprint with a new Houston center designed to help bolster American manufacturing with education and resources.

On Thursday, Apple opened its 20,000-square-foot Advanced Manufacturing Center (AMC) in Houston, Texas. The center is offering free training for small and medium-sized businesses.

The AMC is housed in the same facility that produces Apple’s AI servers. The site is also set to begin Mac mini production in late 2026.

Apple has invested hundreds of millions of dollars into the project, bringing it to fruition in less than nine months.

“We believe in American workers and American ingenuity, and we are moving at an incredible pace because we want to build more than great products. We want to build the future of American manufacturing,” said Cook.

As part of the kickoff event, Apple hosted U.S. Secretary of Commerce Howard Lutnick, U.S. Senator Ted Cruz, Houston Mayor John Whitmire, U.S. Representative Christian Menefee, and Harris County Precinct One Commissioner Rodney Ellis, along with other officials and community partners.

“Houston is grateful to Apple for this significant investment in our city. The Advanced Manufacturing Center will create local jobs and will continue improving the quality of life of Houston residents,” Houston Mayor John Whitmire said of the opening.

“The AMC also recognizes our city as a growing technology hub and solidifies Houston’s leadership in the manufacturing sector of the United States.”

The AMC’s holographic table – Image Credit: Apple

To mark the opening, the business leaders will spend the day immersed in hands-on training. Apple engineers will cover techniques like machine-learning-driven quality control and advanced automation.

Participants will learn how to identify and adapt to production challenges. They will also engage with the lab’s holographic table and advanced factory-floor equipment.

“I am proud that Apple chose Harris County for this investment. My office has consistently fought to bring good jobs within reach of working people and ensure small businesses — especially those historically shut out — have a fair opportunity to compete and grow,” said Harris County Precinct One Commissioner Rodney Ellis.

“At a time when rising costs are squeezing families here and across the country, this new center can help create pathways to greater economic security.”

The site is Apple’s second-largest manufacturing site in the U.S. The first is Apple’s Manufacturing Academy in Detroit.

Since announcing the $600 billion commitment in 2025, Apple and its American Manufacturing Partners have invested reshoring a portion its manufacturing. This includes custom silicon, cover glass, and advanced components.

Spotify would like listeners to know that fake AI artists are a problem.

Unless, apparently, the AI is being used in a licensed product that lets paying subscribers alter music created by actual human beings.

Spotify has announced that it will begin labeling some artist profiles with an AI Persona badge when the public identity presented by that artist does not represent a real person. Beginning in mid-September, those badges will appear on artist profiles, in search results, playlists and individual track listings. More importantly, Spotify says music from AI Personas will not be included by default in editorial or algorithmic recommendations unless listeners have deliberately followed or engaged with them.

That is a meaningful change. It is also the latest turn in Spotify’s increasingly complicated relationship with artificial intelligence.

Related Reading:

Does Spotify Like AI Music or Not?

The answer appears to be: it depends.

Spotify has never broadly banned music merely because AI was involved in creating it. In September 2025, the company explicitly said that it supports artists using AI creatively and that licensed music would be treated equally regardless of the tools used to make it. At the same time, Spotify tightened its rules against unauthorized voice cloning, fraudulent uploads and industrial-scale music spam.

That distinction made some sense.

Using AI during production is not automatically the same thing as generating 5,000 anonymous tracks, impersonating Drake or creating a photorealistic singer who never existed and hoping listeners don’t notice.

Spotify subsequently introduced AI credits that allow artists and distributors to disclose how AI was used in areas including vocals, lyrics and production. As we reported in July, that industry-wide push toward AI Generated and AI Assisted labeling is ultimately about giving listeners some idea of what they are actually hearing.

Then things became considerably more interesting.

Spotify Decided AI Could Also Be a Business

In May, Spotify and Universal Music Group announced a forthcoming generative AI tool that will allow Premium subscribers to create licensed covers and remixes of music from participating artists and songwriters.

It will be sold as a paid add-on.

Universal signed on first, and Merlin followed on August 4, bringing a large collection of independent labels into the program. Spotify says participating artists will have control over whether their music is included and will receive credit and compensation when fans create new versions.

We recently examined that deal and asked the uncomfortable question: if Spotify is worried about AI undermining musicians, why is it preparing to sell consumers tools that let them modify commercially released music in the first place?

The answer increasingly looks like consent and money.

Unauthorized AI impersonation? Bad.

Mass-generated AI sludge designed to siphon royalties? Bad.

A fictional AI artist pretending to be a real person and being pushed into your Discover Weekly playlist? Apparently bad now as well.

AI used under agreements negotiated with Universal, Merlin and other rights holders, with Spotify charging subscribers for the privilege? Welcome aboard.

That does not necessarily make Spotify hypocritical. There is a legitimate distinction between licensed AI tools used with permission and deceptive AI content designed to fool listeners or manipulate the royalty system.

But from the listener’s side, the rules are becoming awfully complicated.

What Is an AI Persona?

Spotify’s new badge is specifically about identity, not whether the music itself was AI generated.

That distinction matters.

An artist can use AI extensively in making a recording without necessarily receiving an AI Persona badge. Conversely, an artist profile portraying a photorealistic fictional human could receive the badge regardless of precisely how the music was created. Spotify says it will initially review profiles that have reached certain audience thresholds rather than relying entirely on voluntary disclosure. Artists can appeal the designation, and Spotify plans to eventually let listeners report suspected AI Personas.

That is considerably more useful than hiding an AI disclosure six menus deep inside the credits. And keeping AI Personas out of recommendations by default may be the most important part of the entire policy.

Spotify’s recommendation engine does not merely help listeners find music; it determines which artists receive enormous amounts of exposure. Removing synthetic personalities from that pipeline makes it harder for AI content farms to compete for attention simply by generating more material than humans possibly can.

Why Listeners Should Care

Listeners should not need forensic training to determine whether the singer Spotify just recommended is an actual person.

That is the larger problem with AI music. The debate is usually framed around copyright, royalties and artist compensation, all of which matter enormously. But streaming services also have a basic responsibility to tell subscribers what they are listening to.

TIDAL recently went further by deciding that wholly AI-generated music can remain on its service but will not receive royalty attribution. Qobuz has also taken a more human-centered approach to curation and AI-generated content. Spotify is choosing a different path: allow AI, disclose more of it, suppress deceptive uses and monetize the versions it can license properly.

There is a logic to that strategy. It just happens to be a logic that becomes considerably easier to understand once somebody is getting paid.

The Bottom Line

Spotify’s AI Persona badge is a positive move for listeners. Clearly identifying fictional artists and keeping them out of recommendations by default should make the service less susceptible to the flood of synthetic content that increasingly threatens genuine music discovery.

But Spotify cannot have this conversation entirely on its own terms.

The company is telling listeners that authenticity matters while simultaneously building a business that will let subscribers use generative AI to alter recordings made by real musicians. Those two positions can coexist if consent, transparency and compensation genuinely remain at the center of the system.

If they don’t, Spotify isn’t drawing an ethical line around AI.

It’s drawing a revenue line.

Related Reading:

The movie theater business is doing something rather inconvenient for everyone who spent the past five years writing its obituary: making a lot more money.

Through August 10, the 2026 domestic box office has generated $6.54 billion, according to Box Office Mojo. That is 19.1% ahead of the same period in 2025 and 28.8% ahead of 2024. It is even running 6.9% ahead of 2023, the year of Barbie and Oppenheimer. For some perspective, the entire domestic box office finished 2025 at $8.66 billion and 2024 at $8.57 billion.

That does not mean everything is back to 2019 levels, and we’ll get to that rather important detail. But the 2026 rebound is no statistical rounding error.

I’ve already seen The Odyssey and Spider-Man: Brand New Day twice. Aside from raising legitimate questions about whether I should be trusted with a movie ticketing app, all four screenings shared something important: I deliberately chose a premium theatrical experience because I wanted something my television and home theater system could not completely reproduce.

Increasingly, I don’t appear to be alone.

Related Reading:

2026 Has More Than One Hero

Spider-Man: Brand New Day has become the monster of the year, reaching $655.1 million domestically and $1.67 billion worldwide in only 10 days. But blaming the entire recovery on Peter Parker would be convenient and wrong.

Toy Story 5 has earned more than $470 million domestically, The Odyssey has crossed $461 million, The Super Mario Galaxy Movie is approaching $430 million, Michael has exceeded $372 million, and Project Hail Mary has generated $344 million. Even Obsession, which didn’t arrive with decades of franchise baggage attached, has surpassed $263 million domestically.

Five 2026 releases have already crossed $1 billion worldwide: Spider-Man: Brand New Day, The Odyssey, Toy Story 5, Michael and The Super Mario Galaxy Movie. That’s more billion-dollar releases than any year since 2019, with plenty of calendar still remaining.

Not everything with a familiar logo has worked. Star Wars: The Mandalorian and Grogu limped to just $177.7 million domestically and $345.2 million worldwide. For comparison, Solo: A Star Wars Story — the film Disney has spent eight years pretending it left in a jacket pocket at Mos Eisley, managed $213.8 million domestically and $392.9 million worldwide back in 2018. Somewhere on Corellia, the Solo crew is high-fiving one another and ordering another round.

But 2026 still has two enormous theatrical wild cards waiting in December. Marvel’s Avengers: Doomsday and Denis Villeneuve’s Dune: Part Three are both scheduled for December 18, and both are being positioned for premium presentation. Dolby currently lists each with Dolby Vision and Dolby Atmos, while Dune: Part Three is already selling advance IMAX 70mm tickets. IMAX also includes Avengers: Doomsday in its 2026 release slate.

That creates a fascinating Christmas problem for theater owners: two films designed to dominate the very IMAX and Dolby Cinema screens that are becoming increasingly valuable. Doomsday looks like the safer bet commercially, even if the footage so far has left me wondering whether Earth’s Mightiest Heroes misplaced the fun somewhere between multiverses; while Villeneuve has turned Dune into exactly the kind of large-scale cinematic event that premium theaters were built to showcase.

The takeaway isn’t that audiences will watch anything Hollywood throws at them again. They clearly won’t. The stronger 2026 slate is giving consumers more reasons to go out, and when the movie feels like an event, they are showing up.

Here’s the Problem: Attendance Hasn’t Really Come Back

This is where the numbers become much more interesting.

U.S. theaters sold an estimated 470.9 million tickets during the first 30 weeks of 2026, compared with 747.3 million over the same stretch of 2019. Placer.ai estimates theater attendance through July was up 8.1% compared with 2025, but remained 27% below 2019.

So how can the box office be booming while substantially fewer people are going to the movies?

Part of the answer is price. EntTelligence puts the average 2026 adult ticket at $13.46, while premium-format admissions such as IMAX average $18.22. More importantly, audiences increasingly appear willing to pay that premium when the experience justifies leaving the house.

And that is where IMAX and Dolby Cinema enter the story.

IMAX Is Having a Ridiculous 2026

Christopher Nolan didn’t merely make The Odyssey for IMAX; he made the first theatrical feature shot entirely with IMAX film cameras. Audiences have responded accordingly.

The Odyssey has now generated $289 million in IMAX box office, making it the format’s highest-grossing release ever. Its $147.3 million domestic IMAX haul is also an all-time company record. July produced $257 million for IMAX worldwide, the highest-grossing month in the company’s more than 50-year history.

This didn’t come out of nowhere. IMAX generated a record $1.28 billion worldwide in 2025, while its domestic box-office share reached 5.2% despite IMAX representing only about 1% of domestic screens. Premium presentation was already growing before Odysseus started sailing around the Mediterranean.

Dolby Cinema Is Seeing the Same Shift

Dolby Cinema generated a record $203 million domestically in 2025. From January 1 through April 6, 2026, revenue had already reached $46 million, 62% higher than the same period a year earlier.

Then Spider-Man showed up.

Brand New Day delivered roughly $10 million from just 177 U.S. Dolby Cinema locations during its opening weekend, the format’s biggest weekend ever. Premium large-format screenings overall accounted for about 24% of Spider-Man’s extraordinary $360 million domestic opening.

That is not a niche audience carrying around calibration meters and arguing about black levels on Reddit. That’s meaningful box-office revenue.

Your Living Room Got Better. Movie Theaters Had To As Well.

The uncomfortable reality for exhibitors is that the average living room became a formidable competitor.

An excellent OLED or Mini-LED television, competent surround system or Atmos soundbar, comfortable sofa and instant access to streaming make the mediocre multiplex experience increasingly difficult to defend. Throw in parking, concessions and the gentleman six seats away illuminating half the auditorium with his phone, and “wait for streaming” starts sounding perfectly reasonable.

But a genuinely great IMAX auditorium or Dolby Cinema changes that equation.

Reuters reports that theater chains are expanding premium large-format screens specifically as audiences become more selective about theatrical trips. AMC said premium-screen demand recently helped produce the highest single-weekend revenue in its 106-year history, with more than 10.2 million customers visiting AMC and Odeon locations.

Streaming didn’t kill movie theaters. Better home entertainment may simply have forced theaters to offer something better.

The 2026 numbers suggest audiences still love going to the movies. They are just becoming far less interested in paying for an ordinary experience they can increasingly duplicate at home.

The movie theater isn’t dying. The ordinary movie theater might be.

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Artificial intelligence has wreaked havoc across secondary and higher education classrooms, leaving educators struggling to figure out how to create cheat-resistant assignments and assessments. While everyone is good at diagnosing the problem, no one seems to have a workable solution. So I designed a free tool to help teachers create uncheatable assessments and avoid the headaches of AI and academic dishonesty.

The Cheat Vulnerability Index

Based on the concepts from my book, course and workshops, I built the Cheat Vulnerability Index, a web app that lets teachers analyze their existing assignments to pinpoint strengths and weaknesses when it comes to vulnerability to cheating. Users upload an assignment, then get a custom report and suggestions for how to make improvements.

I came up with the idea when I noticed that educators struggled to take the ideas from my conference sessions and workshops and implement them in their unique learning contexts. Since I can’t always sit with everyone as they plan lessons or write a syllabus, I wondered how I might scale the concepts and strategies from my content leveraging the power of AI.

I needed to create a brand-new tool that didn’t exist before and to do so with no budget. So I learned to “vibe code,” the method of using natural language in an AI model to create computer code. I hadn’t done coding since I used BASIC in high school, and it took me a while to figure out how it worked, and how to turn the code into an interface on my website. 

I trained the index on concepts from my book Storytelling With Purpose: Digital Projects to Ignite Student Curiosity and a variety of other workshops and articles I’ve published as the pedagogical framework for the assignment analysis. The concepts, processes and strategies it produces are unique to my published work, and create suggestions through that research-based, classroom-tested lens. 

Turns out, vibe coding is a lot like teaching: you begin with deep subject-area expertise (in this case, authentic learning and assessment), create a complex set of detailed instructions and processes for the AI agent to follow (a lesson or unit plan), define what “good” looks like (learning outcomes, standards and rubrics), and how to describe the concepts clearly to an audience (a learning artifact). I was stunned by how well the app turned out, and teachers who have tried it find it useful.

But the biggest takeaway for me is how this experiment revealed the ability of AI tools to remove barriers to learning and how they can facilitate deeper learning through the application of knowledge. 

Despite what they say, not everyone can code — including me. So why should coding (or the cost to hire a team of coders) get in the way of my ideas and creating a useful tool to help others? In the same way, how does a student’s writing ability, processing speed or facility as a public speaker affect their grades if our assessments are in-class essays, timed tests or presentations and debates?  It got me thinking about the obstacles students face expressing their knowledge and how traditional assessments can get in the way of assessing them.

Students may cheat or cut corners for a lot of reasons, not all of which have to do with moral depravity. Designing uncheatable assessments requires more than adjustments to a single assignment — it requires us to expand our definition of success and to rethink what counts as achievement and how we measure it.

Creating Uncheatable Assessments

The Cheat Vulnerability Index is a quick diagnostic that gives teachers and faculty feedback on a single assignment. But to have meaningful, sustained resilience while maintaining rigor and high standards across an entire school year, teachers need to rethink what and how they assess, and foster cultures of integrity that go beyond any one test or unit.

Cheating happens when two conditions are met: when an assignment is cheatable by design and when students have the incentive to cheat. 

Assignments like tests, worksheets or essays are all vulnerable to academic dishonesty simply by their design. AI can write essays, and students can share answers, for example. How can the format of an assignment minimize opportunities for cheating or rely on a combination of multiple metrics that make it less possible?

No matter how well an assignment is designed, if a student really wants to cheat, they will. So instead of trying to surveil students and make our job as educators about policing them, we can create assignments that disincentivize cheating before it becomes a problem in the first place.

There are three ways to do this.

Three Uncheatable Assessment Traits

1. Originality. When we expect students to create the same answers at the same time, we’ve set ourselves up for cheating (with or without AI). Authentic learning experiences result in one-of-a-kind learning artifacts that no other student could copy or use AI to complete entirely.

2. Personal connection. To be truly invested in learning, everyone wants to know “why this matters.” Allowing ways for students to connect curriculum to their lives or community helps them personalize an abstract concept and disincentivizes cheating because they care about the outcome and know it will help them or people in their community. Students should also have multiple opportunities for agency throughout the process.

3. Purpose. What’s the point of students’ hard work? If an assignment ends up in the trash, it sends a powerful message about the value of their effort and your curriculum. Instead, have students create learning artifacts that are designed for users or audiences beyond the classroom. Give students a good reason to complete an assignment with integrity and accuracy. Real stakes beat clever rules.

What’s Next

The problem of academic integrity is more fundamental than preventing students from cheating. AI has forced us to recognize that the old proxies we used to assess students are no longer reliable as evidence of learning (if they ever were), and requires us to redefine what counts as evidence of understanding. Like the Cheat Vulnerability Index I designed that didn’t require me to know the vocabulary or grammar of coding, my learning artifact still required me to build subject-area expertise, identify goals and limits, interrogate what good learning means and take responsibility for the outcome of my work. 

As mathematician Terence Tao says about AI disrupting mathematics research, it’s like we’ve been trying to drive a car on outdated roads made for horses and pedestrians, and it’s revealed potholes and bumps and cracks. AI is now giving us faster cars that lead to big traffic jams and accidents. 

The destination hasn’t changed, just how we get there. What we need isn’t better ways to catch students cheating, but a new pedagogical road forward.